gridMET and GiSSURGO

1 Object

• Summarize what I did for data downloading

2 gridMET

• Target variables:
  • “pr”: precipitation (mm)
  • “tmmn”: Minimum Near-Surface Air Temperature (Kelvin)
  • “tmmx”: Maximum Near-Surface Air Temperature (Kelvin)
  • “pet” : Reference grass evaportranspiration (mm)
• Using well-level data (point data), extract values from gridMET raster data (2007-2015)
  • extract only in season daily data (i.e., from 4/1 to 9/30)
• change those units
  • pr: mm to inch
  • tmmn: K to C
  • tmmx: K to C
  • pet: mm to inch
• calculate daily GDD from tmmn and tmmx
• summarize those variables by year and well id

3 SSURGO

3.1 Description about target variables

• Target variables (reference website: here[https://sdmdataaccess.sc.egov.usda.gov/documents/TablesAndColumnsReport.pdf])
  • sandtotal_r:
    • %
    • Mineral particles 0.05mm to 2.0mm in equivalent diameter as a weight percentage of the less than 2 mm fraction
  • silttotal_r:
    • %
    • Mineral particles 0.002 to 0.05mm in equivalent diameter as a weight percentage of the less than 2.0mm fraction
  • claytotal_r:
    • %
    • Mineral particles less than 0.002mm in equivalent diameter as a weight percentage of the less than 2.0mm fraction
  • ksat_r:
    • um/s
    • The amount of water that would move vertically through a unit area of saturated soil in unit time under unit hydraulic gradient.
  • awc_r:
    • cm/cm
    • The amount of water that an increment of soil depth, inclusive of fragments, can store that is available to plants. AWC is expressed as a volume fraction, and is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension and adjusted for salinity, and fragments.
  • slope_r
    • %
    • The difference in elevation between two points, expressed as a percentage of the distance between those points. (SSM)
• There are many similar variables depends on the particle size
  • Ex) in the sand case
    • sandtotal_ : Mineral particles 0.05mm to 2.0mm in equivalent diameter
    • sandvc_ : Mineral particles 1.0mm to 2.0mm in equivalent diameter
    • sandco_ : Mineral particles 0.5mm to 1.0mm in equivalent diameter
    • sandmed_: Mineral particles 0.25mm to 0.5mm in equivalent
    • sandfine_ : Mineral particles 0.10 to 0.25mm in equivalent diameter
    • sandvf_ : Mineral particles 0.05 to 0.10mm in equivalent diameter

3.2 Process

• make buffer (400 m radius circles around well, which covers about 50 ha of land) for each well

source("./GitControlled/Codes/0_1_ls_packages.R")
source("./GitControlled/Codes/0_0_functions.R")
# /*===== point data for well location =====*/
data_w_LR_TB <- readRDS("./Shared/Data/WaterAnalysis/data_w_LR_TB.rds")

well_sf <- data_w_LR_TB %>%
  unique(.,by="wellid") %>%
  .[,.(wellid, longdd, latdd)] %>%
  # --- the geographic coordinate in the well data is NAD83 (espg=4269)--- #
  st_as_sf(., coords = c("longdd","latdd"), crs = 4269)


# /*===== make buffer around each well  =====*/
well_buffer_sf <- well_sf %>%
  #--- project to WGS UTM 14 (Cartesian 2D CS covering NE)---#
  st_transform(32614) %>%
  # --- make buffers--- #
  # 400 m radius circles around well, which covers about 50 ha of land
  st_buffer(., dist = 400)
 
ggplot()+
  geom_sf(data=well_sf, size=0.5) +
  geom_sf(data=well_buffer_sf, color="red", fill=NA)

well_buffer_sp <- as(well_buffer_sf, "Spatial")

• Then, apply get_ssurgo_props() for each well buffer.

get_ssurgo_props <- function(field, vars, summarize = FALSE) {
  # field=unique_well_sp; vars = c("sandtotal_r", "claytotal_r")
  # Get SSURGO mukeys for polygon intersection
  ssurgo_geom <-
    SDA_spatialQuery(
      geom = field,
      what = "geom",
      db = "SSURGO",
      geomIntersection = TRUE
    ) %>%
    st_as_sf()
    mutate(
      area = as.numeric(st_area(.)),
      area_weight = area / sum(area)
    )

  # Get soil properties for each mukey
  mukeydata <-
    get_SDA_property(
      property = vars,
      method = "Weighted Average",
      mukeys = ssurgo_geom$mukey,
      top_depth = 0,
      bottom_depth = 150
    )

  ssurgo_data <- left_join(ssurgo_geom, mukeydata, by = "mukey")
  
  if (summarize == TRUE) {
    ssurgo_data_sum <-
      ssurgo_data %>%
      data.table() %>%
      .[,
        lapply(.SD, weighted.mean, w = area_weight),
        .SDcols = vars
      ]
    return(ssurgo_data_sum)
  } else {
    return(ssurgo_data)
  }
}

3.2.1 Example: SSURGO polygon data for an example field

library(soilDB)
library(aqp)
library(sp)

ex_well_bf <- well_buffer_sf[5,]
ex_well_bf_sp <- as(ex_well_bf, "Spatial")

ssurgo_geom <-
    SDA_spatialQuery(
      geom = ex_well_bf_sp,
      what = "geom",
      db = "SSURGO",
      geomIntersection = TRUE
    ) %>%
    st_as_sf()%>%
    dplyr::mutate(
      area = as.numeric(st_area(.)),
      area_weight = area / sum(area)
    )

# /*===== visualization =====*/
ggplot()+
  geom_sf(data=ssurgo_geom, aes(fill=factor(mukey)), color="blue", alpha=0.5)+
  geom_sf(data= well_sf[5,], fill=NA)